1. Personalised Support for Reflective Learning in Fire Risk Assessment Wichai Eamsinvattana Supervisors: Vania Dimitrova, School of Computing David Allen, Business School

2. Presentation Plan Motivation: Reflective Learning for Fire Risk Assessment Goal and Research Questions PORML Framework Contribution Conclusions

3. Training needs –Lack of effective training models in Fire and Rescue Services (FRS) Importance of risk assessment skills –Crew commanders risk assessment skills have major impact on the efficiency and effectiveness of dealing with fire accidents Need for personalisation –Crew commanders come from diverse backgrounds, have different experience, frequently changing jobs, and often do not have enough practice Motivation

4. Disconnected from the real context –Simulated environment –Off-site –Not very effective Training at Fire and Rescue Services

5. Conditions rapidly change and various aspects (e.g. road traffic, lethal chemicals) Previous experience is important Crew commanders risk assessment is crucial Many crew commanders are inexperienced Review of the risk assessment normally takes a lot of time (a month, 3 months or more) (interviews with UK FRS representative) What Happens in Reality

6. Problem: Can we find intelligent ways to capture the real risk assessment activities and create appropriate learning scenarios? Our Approach: Personalised mobile reflective learning The Problem & Our Approach

7. Mobile technology to support decision making at FRS Aimed at decision making but learning is not considered Virtual reality to support reflective learning Learning by reflecting on FRS activities but disconnected from the real environment No research has been conducted to use mobile technologies for reflective on-the-job learning. Existing Research (From a thesis in School of Education, University of Leeds)

8. Goal: Examine how the Activity Theory can be utilised to develop personalised mobile learning environments to support reflective on-the-job training at Fire and Rescue Services. Goal and Research Questions Research Questions: Can we use Activity Theory to inform the design of an intelligent agent that captures a users risk assessment experience? How can a holistic model of context be developed by exploiting an ontological model of generic risk assessment and semantic enhanced location information? Can we design context-adapted dialogue to capture a users risk assessment experience and promote reflection?

9. Personalised On-the-job Reflective Mobile Learning (PORML) Framework Capture Semantic Location

10. Capturing Semantic Location

11. Personalised On-the-job Reflective Mobile Learning (PORML) Framework Domain Ontology (DRA Ontology) for Fire Risk Assessment

12. Creating Domain Ontology for Fire Risk Assessment haveRole Assess Hazardous Substance Crew Member

13. Personalised On-the-job Reflective Mobile Learning (PORML) Framework A Main Part of PORML (Dialogue Interaction)

14. Using Dialogue Interaction to Capture User Experience (Activity)

16. PORML Dialogue Example Collect Basic Information Dialog: AGENTWhat was the incident place name you deal with? Dialog: UserIt was aSixBells Pub Confirm/SendSixBells Car Park Garage1 Building11 Building11a BDa Collect Initial User and Location Information Collect Basic Information Dialog: AGENTHow were the weather conditions during fighting the Chimney Fire? Dialog: UserWeatherRain, WindLow, VisibilityGood FreezeHighBad Confirm/SendRainLowGood Snow Sunny Collect Basic Information Dialog: AGENTWhich fire type do you want to assess? Dialog: UserI want to assessChimney Fire Building Fire Confirm/SendChimney Fire Farm Fire High Rise Building Fire Public Entertainment Venue Fire Rural Area Fire Secure Accommodation Fire Collect Basic Information Dialog: AGENTWhat was the incident place name you deal with? Dialog: UserIt was aSixBells Pub Confirm/SendSixBells Car Park Garage1 Building11 Building11a BDa

17. Example on iPhone 3G Used in Summative Evaluation

18. A new context modelling algorithms that capture a users risk assessment experience based on semantic-enhanced geographic data and an ontological model of general risk assessment activity Demonstration how these algorithms can be utilised in a novel pedagogical environment to promote reflective learning Examination of whether the new technological solutions could be deployed in the Fire and Rescue Services training practice Contribution

19. This work presents a new opportunity for personalised reflective learning using dialogue to reflect the fire risk assessment activity in emergency services based on the users real job experiences The potential benefits of modelling this assessment context are quick risk assessment linked to the real situation The user experiences and location environments are involved and taken into account the risk assessment activities Conclusions Thank you!